我有一个数据集，包含以下列：年龄（浮点类型）、性别（字符串类型）、地区（字符串类型）和费用（浮点类型）。
我想使用年龄、性别和地区作为特征来预测费用，如何在scikit-learn中实现这一点？
我尝试了一些方法，但显示了 "ValueError: could not convert string to float: 'northwest' "
import pandas as pdimport numpy as npdf = pd.read_csv('Desktop/insurance.csv')X = df.loc[:,['age','sex','region']].valuesy = df.loc[:,['charges']].valuesfrom sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)from sklearn import svmclf = svm.SVC(C=1.0, cache_size=200,decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf')clf.fit(X_train, y_train)回答：
region列包含字符串，这些字符串不能直接在SVM分类器中使用，因为它们不是向量。
因此，你需要将这一列转换为SVM可以使用的形式。这里有一个例子，通过将region转换为分类系列：
import pandas as pdfrom sklearn import svmfrom sklearn.model_selection import train_test_splitdf = pd.DataFrame({'age':[20,30,40,50], 'sex':['male','female','female','male'], 'region':['northwest','southwest','northeast','southeast'], 'charges':[1000,1000,2000,2000]})df.sex = (df.sex == 'female')df.region = pd.Categorical(df.region)df.region = df.region.cat.codesX = df.loc[:,['age','sex','region']]y = df.loc[:,['charges']]X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)clf = svm.SVC(C=1.0, cache_size=200,decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf')clf.fit(X_train, y_train)解决这个问题另一种方法是使用独热编码：
import pandas as pdfrom sklearn import svmfrom sklearn.model_selection import train_test_splitdf = pd.DataFrame({'age':[20,30,40,50], 'sex':['male','female','female','male'], 'region':['northwest','southwest','northeast','southeast'], 'charges':[1000,1000,2000,2000]})df.sex = (df.sex == 'female')df = pd.concat([df,pd.get_dummies(df.region)],axis = 1).drop('region',1)X = df.drop('charges',1)y = df.chargesX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)clf = svm.SVC(C=1.0, cache_size=200,decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf')clf.fit(X_train, y_train)还有一个方法是执行标签编码：
from sklearn.preprocessing import LabelEncoderle = LabelEncoder()df.region = le.fit_transform(df.region)当然，这些方法并不全面，它们的表现根据你的问题不同而有所不同。
使用非数值数据是一个非平凡的问题，需要对现有技术有一定的了解（我建议你去kaggle的论坛上搜索，那里可以找到有价值的信息）。
